Quick overview
Job description
Key Responsibility
- Architect and lead development of production AI products including intelligent chatbots, document processing systems, and agentic workflows using Python and modern AI frameworks
- Design and implement our centralized AI platform including model routing, provider management, vector search, and AI application frameworks with seamless MCP (Model Context Protocol) integrations
- Build scalable AI products that integrate with diverse technologies including accounting systems, document repositories, and external APIs while maintaining robust monitoring and observability
- Master context engineering and system design for AI applications, ensuring optimal information retrieval, context assembly, and multi-turn conversation management
- Collaborate with Product, Engineering, and Security teams to ensure AI products are robust, compliant, and aligned with business objectives in the regulated accounting space
- Provide technical leadership and mentorship to the growing AI team, establishing best practices for AI product development, deployment, and governance
Required Qualifications
- 10+ years of professional software engineering experience with 4+ years focused on building backend for production applications
- Mastery of Python, Familiarity with some AI application frameworks, context engineering, and scalable system design for AI products
- Expertise in designing products that integrate with multiple technologies, APIs, and data sources in cloud-native environments (AWS preferred)
- Strong desire to develop deep hands-on experience with LLM APIs, retrieval-augmented generation (RAG), conversational AI, document processing, and MCP integrations
- Proven ability to lead tech product initiatives, establish technical standards and communicate complex system designs to both technical and business stakeholders
Nice to Haves/Other
- Experience building chatbots or conversational AI products in production
- Background in system integration, API design, or enterprise software platforms
- Familiarity with accounting workflows and financial data processing
- Experience with AI observability, debugging tools, and production AI monitoring
- Hands-on experience with advanced RAG architectures, reranking systems, and retrieval optimization techniques
- Knowledge of reinforcement learning from human feedback (RLHF) or other RL techniques for improving AI product performance
- Experience building AI platform components like embedding pipelines, semantic search systems, or multi-modal processing frameworks
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